Last time, I was confused about which buildings we had obtained waste-water data for. This week, I’ve removed the off-campus site (Robert Guidon Hall) and retained the other 6 on-campus sites (raw data below).
We probably want to summarise our swabs at the weekly (or biweekly?) level for comparison to the waste-water signal. This is what the weekly campus-wide time-series looks like compared with our weekly campus-wide swab positivity:
If we look at the relationship between swabs and waste-water on weeks where both results are available, we see that they have a positive correlation.
The Pearson’s r (linear correlation) is 0.66 (0.38-0.83). The Spearman’s r (rank correlation) is 0.502.. The Kendall’s Tau is 0.36.
Pearson’s r is more affected by outliers. Spearman’s r or Kendall’s Tau are probably the more reliable measures of correlation between these variables.
(Data doesn’t appear normally distributed, btw)
This plot shows the counts of positive (red) and negative (yellow) samples collected at each facility over time (x-axis). Samples that could not be tested are shown in navy. Only flocked swabs are shown. (Other sponge swabs were collected on 2022-04-28 were for comparing flocked and sponge swabs.)
This plot shows the counts of positive and negative samples collected at each facility over time.
This section contains results from modeling SARS-CoV-2 cases at UO using swab-PCR results as a predictor.
We created a random intercepts logistic regression model with the occurrence of cases (binary) as outcome and swab results for the previous week (the proportion of positive swabs) as predictor. The model has a random intercept for each site.
Our model formula is
cases ~ positives[lag 1 week] + (1|site).
The model is fit as follows:
swab_model <-
blme::bglmer(
cases_binary ~ detection_lag_1week + (1 | site),
data = uo_sites,
family = binomial
)
These plots show the swab results, cases, and predictions by the
current model.
These tables show the model coefficients and statistics.
| nobs | sigma | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|
| 84 | 1 | -24.52 | 55.04 | 62.33 | 42.83 | 81 |
| effect | group | term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|---|---|
| fixed | NA | (Intercept) | -3.301 | 0.804 | -4.107 | 0.0000401 |
| fixed | NA | detection_lag1w | 7.114 | 2.112 | 3.369 | 0.0007549 |
| ran_pars | site | sd__(Intercept) | 1.067 | NA | NA | NA |
| Relation between UO cases (y/n) and proportion of positive swabs the previous week | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.04 | 0.01 – 0.18 | <0.001 |
| Swabs @ t-1week | 1229.31 | 19.59 – 77128.28 | 0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 site | 1.14 | ||
| ICC | 0.26 | ||
| N site | 6 | ||
| Observations | 84 | ||
| Marginal R2 / Conditional R2 | 0.378 / 0.538 | ||
This plot shows the modelling data as points (detection lagged 1 week and cases at a given site- y/n), as well as how the probability of future cases varies by the previous weeks detection level and site, according to our model (curves).
This plot shows the odds ratios for the intercepts of the model (an intercept for each site).
This panel shows the cases counts at UO over the course of our sampling period. The case data shown represents the days on which a positive test was reported (black rug lines) and the presumed start of transmissiblity for each case (red lines).
This panel shows linked data from swab results, CO2
readings, and wifi traffic (number of users @ peak, daily) during our
study period. Unfortunately, we do not have wifi data for 90U.
This panel shows a time-series of the daily peak number of wifi users
at UO facilities. Sampling days are highlighted in blue.